How much does vaccination reduce the rate of HBV infection in Iranian population? a Bayesian adjustment analysis

AIM: The aim of this research was to estimate the changing rate of odds ratio (OR) by varying degrees of hepatitis B virus (HBV) underreporting. BACKGROUND: Data registering is usually associated with extensive errors such as misclassification, under-reporting, missing data due to lack of co-operation, error prone factors, and in medical studies, inadequate diagnosis of physicians or low accuracy of laboratory tests. In the present study, which discuss the actual impact of vaccination on HBV prevention, exposure and response were prone to various errors. Furthermore, some people in the community are possibly infected to the virus while were not reported in the count of patients with HBV infection. METHODS: This was a case control study. Cases included patients with HBV referring to the gastroenterology and liver disease research center. The control group included patients without HBV who underwent a fatty liver test at Taleghani hospital laboratory. Bayesian approach and Gibbs sampling algorithm were used to estimate OR. RESULTS: According to results, misclassification rate was mild in raw data, but with an increase in degree of underreporting for 50 and 500 of unreported cases, OR increased by about half and more than double, respectively, while sensitivity diminished strikingly. CONCLUSION: Our analysis asserted that knowing the degree of underreporting is essential to accurately calculate OR and sensitivity. In addition, despite varying OR in different samples, overall the results were similar according to the pattern of exposure and response association.